Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, 6728 Szeged, Hungary.
National Laboratory of Biotechnology, Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, 6726 Szeged, Hungary.
Proc Natl Acad Sci U S A. 2023 Aug 29;120(35):e2302147120. doi: 10.1073/pnas.2302147120. Epub 2023 Aug 21.
Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.
代谢物水平塑造了细胞生理和疾病易感性,但控制代谢组进化的一般原则在很大程度上仍是未知的。在这里,我们引入了一种衡量相关物种中单个代谢物水平保守性的方法。通过分析来自进化上多样化的哺乳动物和果蝇的多物种组织代谢组数据集,我们表明保守性在代谢物之间广泛变化。三个主要的功能特性,即代谢物丰度、必需性和与人类疾病的关联,预测了保守性,突出了驱动代谢组和蛋白质序列保守性的进化力量之间的惊人平行关系。代谢网络模拟再现了这些总体模式,并揭示了丰富的代谢物由于其与网络中关键代谢通量的强耦合而高度保守。最后,我们表明,代谢疾病的生物标志物可以根据进化保守性与其他代谢物区分开来,而无需任何先前的临床知识。总的来说,这项研究揭示了支配动物代谢进化的简单规则,并表明物种之间的大多数组织代谢组差异是被允许的,而不是被自然选择所偏好的。更广泛地说,我们的工作为利用进化信息来识别生物标志物以及在个体患者中检测致病代谢组改变铺平了道路。